In [28]:
from tensorflow.keras.layers import LSTM, Dense, Dropout
from tensorflow.keras.models import Sequential
from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt
from sklearn.metrics import mean_squared_error
import numpy as np
def train_and_evaluate_model(X_train, y_train, X_test, y_test, lstm_units, dropout_rate, learning_rate, epochs=50, batch_size=64):
model = Sequential()
model.add(LSTM(lstm_units, return_sequences=True, input_shape=(X_train.shape[1], X_train.shape[2])))
model.add(Dropout(dropout_rate))
model.add(LSTM(lstm_units))
model.add(Dropout(dropout_rate))
model.add(Dense(1))
optimizer = Adam(learning_rate=learning_rate)
model.compile(loss='mean_squared_error', optimizer=optimizer)
history = model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test), verbose=1, shuffle=False)
train_predict = model.predict(X_train)
test_predict = model.predict(X_test)
train_rmse = np.sqrt(mean_squared_error(y_train, train_predict))
test_rmse = np.sqrt(mean_squared_error(y_test, test_predict))
plt.plot(history.history['loss'], label='Train Loss')
plt.plot(history.history['val_loss'], label='Test Loss')
plt.title('Training and Validation Loss')
plt.legend()
plt.show()
return train_rmse, test_rmse
lstm_units = 100 # From Optuna
dropout_rate = 0.3452337125154388 # From Optuna
learning_rate = 0.0006220505444542745 # From Optuna
train_rmse, test_rmse = train_and_evaluate_model(
X_train, y_train, X_test, y_test,
lstm_units=lstm_units,
dropout_rate=dropout_rate,
learning_rate=learning_rate
)
print(f"Train RMSE: {train_rmse}, Test RMSE: {test_rmse}")
74/74 ━━━━━━━━━━━━━━━━━━━━ 1s 6ms/step 19/19 ━━━━━━━━━━━━━━━━━━━━ 0s 2ms/step
Units: 50, Layers: 1 => Train RMSE: 0.03512533177352272, Test RMSE: 0.020643031084390882
C:\Users\Luke Holmes\anaconda3\Lib\site-packages\keras\src\layers\rnn\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(**kwargs)
74/74 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step 19/19 ━━━━━━━━━━━━━━━━━━━━ 0s 4ms/step
Units: 50, Layers: 2 => Train RMSE: 0.0637469685834429, Test RMSE: 0.028491347000281594
C:\Users\Luke Holmes\anaconda3\Lib\site-packages\keras\src\layers\rnn\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(**kwargs)
74/74 ━━━━━━━━━━━━━━━━━━━━ 1s 9ms/step 19/19 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step
Units: 50, Layers: 3 => Train RMSE: 0.046748635757175284, Test RMSE: 0.026319640058699755
C:\Users\Luke Holmes\anaconda3\Lib\site-packages\keras\src\layers\rnn\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(**kwargs)
74/74 ━━━━━━━━━━━━━━━━━━━━ 1s 5ms/step 19/19 ━━━━━━━━━━━━━━━━━━━━ 0s 3ms/step
Units: 100, Layers: 1 => Train RMSE: 0.019572349799368467, Test RMSE: 0.021077017916277595
C:\Users\Luke Holmes\anaconda3\Lib\site-packages\keras\src\layers\rnn\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(**kwargs)
74/74 ━━━━━━━━━━━━━━━━━━━━ 1s 10ms/step 19/19 ━━━━━━━━━━━━━━━━━━━━ 0s 5ms/step
Units: 100, Layers: 2 => Train RMSE: 0.07661178268558169, Test RMSE: 0.023474108356920143
C:\Users\Luke Holmes\anaconda3\Lib\site-packages\keras\src\layers\rnn\rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead. super().__init__(**kwargs)
74/74 ━━━━━━━━━━━━━━━━━━━━ 1s 13ms/step 19/19 ━━━━━━━━━━━━━━━━━━━━ 0s 6ms/step
Units: 100, Layers: 3 => Train RMSE: 0.04039341945663431, Test RMSE: 0.034419499330793774
